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Solving Task Scheduling Problem in the Cloud Using a Hybrid Particle Swarm Optimization Approach
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Author(s): Salmi Cheikh (Laboratoire de Modélisation, d'Optimisation et de Système Électroniques (LIMOSE), University of M'Hammed Bougara, Boumerdès, Algeria)and Jessie J. Walker (STEM Resources, USA)
Copyright: 2022
Volume: 13
Issue: 1
Pages: 25
Source title:
International Journal of Applied Metaheuristic Computing (IJAMC)
Editor(s)-in-Chief: Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/IJAMC.2022010105
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Abstract
Synergistic confluence of pervasive sensing, computing, and networking is generating heterogeneous data at unprecedented scale and complexity. Cloud computing has emergered in the last two decades as a unique storage and computing resource to support a diverse assortment of applications. Numerous organizations are migrating to the cloud to store and process their information. When the cloud infrastructures and resources are insufficient to satisfy end-users requests, scheduling mechanisms are required. Task scheduling, especially in a distributed and heterogeneous system is an NP-hard problem since various task parameters must be considered for an appropriate scheduling. In this paper we propose a hybrid PSO and extremal optimization-based approach to resolve task scheduling in the cloud. The algorithm optimizes makespan which is an important criterion to schedule a number of tasks on different Virtual Machines. Experiments on synthetic and real-life workloads show the capability of the method to successfully schedule task and outperforms many known methods of the state of the art.
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